In this article, we mainly study the depth and width of autoencoders consisting of rectified linear unit (ReLU) activation functions. An autoencoder is a layered neural network consisting of an ...

This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each autoencoder consists of two, possibly deep, neural ...

Simple autoencoder: Simple autoencoder is the simplest model to start with. It is consist of an encoder model with a single fully-connected layer, and a decoder model also with a single ...

For example, in the early days of neural networks, it was not known if an exotic hidden layer activation function such as arctan() would have a big effect (it doesn't). Similarly, it's not known, at ...

The second part of the autoencoder generates a cleaned version of the input. The first part of an autoencoder is called the encoder component, and the second part is called the decoder. To use an ...

A variational autoencoder (Kingma and Welling, 2013; Doersch, 2016) consists of an encoder and a decoder. We propose the following architecture for them. The encoder consists of a convolutional and a ...